# Copyright 2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
import sys
import numpy as np
import torch
import torch.onnx

from modeling.deeplab import *

def main(input_file, output_file):
    model = DeepLab(num_classes=21,
                backbone="resnet",
                output_stride=16,
                sync_bn=False,
                freeze_bn=False)
    checkpoint = torch.load(input_file, map_location='cpu')
    model.load_state_dict(checkpoint['state_dict'])
    model.eval()
    input_names = ["actual_input_1"]
    output_names = ["output1"]
    dummy_input = torch.randn(1, 3, 513, 513)
    dynamic_axes = {'actual_input_1':{0:'-1'},'output1':{0:'-1'}}
    torch.onnx.export(model, dummy_input, output_file, input_names=input_names, output_names=output_names,dynamic_axes = dynamic_axes, opset_version=11)

if __name__ == "__main__":
    input_file = sys.argv[1]
    output_file = sys.argv[2]
    main(input_file, output_file)
